#!/usr/bin/env python3
"""
Test Multiple Models via API for Crossword Clue Generation
Compare various models and find the best performer.
"""

import sys
import logging
from pathlib import Path

# Add hack directory to path for imports
sys.path.insert(0, str(Path(__file__).parent))

try:
    from api_clue_generator import APIClueGenerator
    API_AVAILABLE = True
except ImportError as e:
    print(f"❌ Import error: {e}")
    API_AVAILABLE = False

# Set up logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)


def test_multiple_models():
    """Test multiple models via API and compare results."""
    if not API_AVAILABLE:
        print("❌ Cannot run test - API generator not available")
        return
    
    print("🧪 Testing Multiple Models via Hugging Face API")
    print("=" * 60)
    
    # Initialize API generator
    generator = APIClueGenerator()
    
    print(f"🎯 Testing {len(generator.models)} models:")
    for i, (key, model) in enumerate(generator.models.items(), 1):
        print(f"  {i}. {key} ({model})")
    
    # Test cases for comprehensive evaluation
    test_cases = [
        # Cases that failed with local models
        ("CAT", "animals"),
        ("BATSMAN", "cricket"),
        ("SWIMMING", "sports"), 
        ("AIRPORT", "transportation"),
        ("DATABASE", "technology"),
        
        # Additional challenging cases
        ("VIOLIN", "music"),
        ("PIZZA", "food"),
        ("SCIENTIST", "science"),
        ("MOUNTAIN", "geography"),
        ("ELEPHANT", "animals"),
    ]
    
    print(f"\n📊 Testing {len(test_cases)} word-topic combinations")
    print("=" * 60)
    
    # Track results for each model
    model_scores = {model_key: {"total": 0, "excellent": 0, "good": 0, "poor": 0, "failed": 0} 
                   for model_key in generator.models.keys()}
    all_results = []
    
    for i, (word, topic) in enumerate(test_cases, 1):
        print(f"\n📝 Test {i}/{len(test_cases)}: '{word}' + '{topic}'")
        print("-" * 50)
        
        try:
            # Generate clues with all models
            results = generator.generate_clue(word, topic)
            test_result = {"word": word, "topic": topic, "results": {}}
            
            # Evaluate each model's response
            for model_key, clue in results.items():
                if clue:
                    quality, score = generator.evaluate_clue_quality(word, clue)
                    test_result["results"][model_key] = {"clue": clue, "quality": quality, "score": score}
                    
                    # Update model statistics
                    model_scores[model_key]["total"] += 1
                    if quality == "EXCELLENT":
                        model_scores[model_key]["excellent"] += 1
                    elif quality == "GOOD":
                        model_scores[model_key]["good"] += 1
                    elif quality == "ACCEPTABLE":
                        model_scores[model_key]["good"] += 1  # Count as good
                    else:
                        model_scores[model_key]["poor"] += 1
                        
                    print(f"  {model_key:20} | {quality:10} | {clue}")
                else:
                    model_scores[model_key]["failed"] += 1
                    test_result["results"][model_key] = {"clue": None, "quality": "FAILED", "score": 0.0}
                    print(f"  {model_key:20} | FAILED     | No response")
            
            all_results.append(test_result)
            
        except Exception as e:
            print(f"❌ Error in test {i}: {e}")
    
    # Calculate final scores and rankings
    print(f"\n" + "=" * 60)
    print("📊 FINAL MODEL COMPARISON RESULTS")
    print("=" * 60)
    
    model_rankings = []
    for model_key, stats in model_scores.items():
        if stats["total"] > 0:
            success_rate = ((stats["excellent"] + stats["good"]) / len(test_cases)) * 100
            excellence_rate = (stats["excellent"] / len(test_cases)) * 100
            failure_rate = (stats["failed"] / len(test_cases)) * 100
        else:
            success_rate = excellence_rate = failure_rate = 0
        
        model_rankings.append({
            "model": model_key,
            "success_rate": success_rate,
            "excellence_rate": excellence_rate,
            "failure_rate": failure_rate,
            "stats": stats
        })
    
    # Sort by success rate, then by excellence rate
    model_rankings.sort(key=lambda x: (x["success_rate"], x["excellence_rate"]), reverse=True)
    
    print(f"{'Rank':4} {'Model':25} {'Success%':8} {'Excel%':7} {'Fail%':6} {'E':2} {'G':2} {'P':2} {'F':2}")
    print("-" * 75)
    
    for i, ranking in enumerate(model_rankings, 1):
        model = ranking["model"]
        success = ranking["success_rate"]
        excel = ranking["excellence_rate"]
        fail = ranking["failure_rate"]
        stats = ranking["stats"]
        
        print(f"{i:4} {model:25} {success:7.1f} {excel:6.1f} {fail:5.1f} "
              f"{stats['excellent']:2} {stats['good']:2} {stats['poor']:2} {stats['failed']:2}")
    
    # Show best results
    if model_rankings:
        best_model = model_rankings[0]
        print(f"\n🏆 BEST PERFORMING MODEL: {best_model['model']}")
        print(f"   Success Rate: {best_model['success_rate']:.1f}%")
        print(f"   Excellence Rate: {best_model['excellence_rate']:.1f}%")
        
        if best_model['success_rate'] >= 70:
            print("🎉 EXCELLENT! This model is ready for production use!")
        elif best_model['success_rate'] >= 50:
            print("🔄 Good results! This model shows promise for crossword generation")
        else:
            print("⚠️  Moderate results. May need prompt refinement or different approach")
    
    # Show some example excellent clues
    print(f"\n🌟 BEST CLUE EXAMPLES:")
    print("-" * 40)
    excellent_examples = []
    for result in all_results:
        for model_key, res in result["results"].items():
            if res["quality"] == "EXCELLENT":
                excellent_examples.append((result["word"], result["topic"], res["clue"], model_key))
    
    for word, topic, clue, model in excellent_examples[:5]:  # Show top 5
        print(f"  {word} + {topic}: \"{clue}\" ({model})")
    
    return model_rankings


def main():
    """Run the multiple model comparison test."""
    rankings = test_multiple_models()
    
    if rankings:
        print(f"\n💡 RECOMMENDATION:")
        best = rankings[0]
        print(f"Use '{best['model']}' as your primary clue generation model.")
        print(f"It achieved {best['success_rate']:.1f}% success rate with {best['excellence_rate']:.1f}% excellent clues.")


if __name__ == "__main__":
    main()